Computational Reproducibility in Finance: Evidence from 1,000 Tests

Abstract

Abstract We analyze the computational reproducibility of more than 1,000 empirical answers to 6 research questions in finance provided by 168 research teams. Running the researchers’ code on the same raw data regenerates exactly the same results only 52% of the time. Reproducibility is higher for researchers with better coding skills and those exerting more effort. It is lower for more technical research questions, more complex code, and results lying in the tails of the distribution. Researchers exhibit overconfidence when assessing the reproducibility of their own research. We provide guidelines for finance researchers and discuss implementable reproducibility policies for academic journals.